Image-Wide Artifacts Reduction Caused by High Attenuating Objects in Ct Deploying Voxel Tissue Class

ABSTRACT

A reconstruction processor ( 34 ) reconstructs acquired projection data (S) into an uncorrected reconstructed image (T). A classifying algorithm ( 66 ) classifies pixels of the uncorrected reconstructed image (T) at least into metal, bone, tissue, and air pixel classes. A clustering algorithm ( 60 ) iteratively assigns pixels to best fit classes. A pixel replacement algorithm ( 70 ) replaces metal class pixels of the uncorrected reconstructed image (T) with pixel values of the bone density class to generate a metal free image. A morphological algorithm ( 80 ) applies prior knowledge of the subject&#39;s anatomy to the metal free image to correct the shapes of the class regions to generate a model tomogram image. A forward projector ( 88 ) forward projects the model tomogram image to generate model projection data (S model ). A corrupted rays identifying algorithm ( 100 ) identifies the rays in the original projection data (S) which lie through the regions containing metal objects. A corrupted rays replacement algorithm ( 102 ) replaces the corrupted regions with corresponding regions of the model projection data to generate corrected projection data (S). The reconstruction processor ( 34 ) reconstructs the corrected projection data (S) into a corrected reconstructed 3D image (T′).

The present application relates to the diagnostic imaging arts. It findsparticular application in computed tomography imaging of a subject thatincludes high density regions such as metal implants, dental fillings,and the like, and will be described with particular reference thereto.However, it also finds application in other types of tomographic imagingsuch as single photon emission computed tomography (SPECT), positronemission tomography (PET), three-dimensional x-ray imaging, and thelike.

In CT imaging, high absorbing objects such as metal bodies may causesignificant artifacts, which may compromise the diagnostic value of theimage. Metal artifacts arise when the imaged region of interest containsmetal implants, dental fillings, bullets, or other articles of highradiation absorption which prevent the x-rays from fully penetrating thesubject. Projection line integrals passing through the regions of highdensity are so highly attenuated by the high density regions that dataabout other regions along the line integral are lost or overshadowed.This leads to substantial measurement errors. The filteredbackprojection or other reconstruction process translates thesemeasurement errors into image artifacts, e.g. streaks which emanate fromthe high intensity region. The streaks dramatically deteriorate imagequality and can obliterate structure of the region.

A previous method for correcting metal artifacts includes performingfiltered backprojection to generate an uncorrected reconstructed image,identifying a region of high density in the uncorrected reconstructedimage, and replacing projections that pass through the high densityregion with synthetic projection data having reduced absorptionattenuation values. The corrected projection data again undergoesfiltered backprojection to produce a corrected reconstructed image.

This known method works well for certain imaging applications in whichthere is a single, well-defined high density region surrounded by muchlower density tissue. It does not work well, however, with a pluralityof high density regions, or where there are medium density regions inaddition to the high density region. For such composite imagingsubjects, metal artifacts are reduced but remain very visible in thecorrected reconstructed image, especially between high density andmedium density regions. In medical imaging applications, medium densityregions typically correspond to bone while high density regionstypically correspond to metal implants, dental fillings, operation clips(used in certain interventional computed tomography applications),prosthesis devices, and the like. Hence, in medical computed tomographyimaging, the region of interest commonly contains medium densityregions.

There is a need for an automated technique that compensates for metalartifacts in the image yet is simple, cost effective and easy toimplement. The present invention contemplates a method and apparatusthat overcomes the aforementioned limitations and others.

According to one aspect of the present application, a diagnostic imagingsystem which automatically corrects metal artifacts in an uncorrectedtomographic image caused by high attenuating objects is disclosed. Ameans clusters pixels of the uncorrected tomographic image. A meansclassifies pixels of the uncorrected reconstructed image into at leastmetal, bone, tissue, and air pixel classes to generate a classifiedimage. A means replaces metal class pixels of the classified image withpixel values of another pixel class to generate a metal free classifiedimage. A means forward projects the metal free classified image togenerate a model projection data. A means identifies corrupted regionsof original projection data contributing to the pixels of the metalclass. A means replaces the identified corrupted regions withcorresponding regions of the model projection data to generate correctedprojection data which is reconstructed by a reconstruction means into acorrected reconstructed image.

According to another aspect of the present application, a method forautomatically correcting metal artifacts in an uncorrected tomographicimage caused by high attenuating objects is disclosed. Pixels of theuncorrected tomographic image are clustered. Pixels of the uncorrectedreconstructed image are classified into at least metal, bone, tissue,and air pixel classes to generate a classified image. Metal class pixelsof the classified image are replaced with pixel values of another pixelclass to generate a metal free classified image. The metal freeclassified image is forward projected to generate a model projectiondata. Corrupted regions of original projection data contributing to thepixels of the metal class are identified. The identified corruptedregions are replaced with corresponding regions of the model projectiondata to generate corrected projection data. The corrected projectiondata is reconstructed into a corrected reconstructed image.

One advantage of the present application resides in automaticallycompensating the image artifacts caused by high attenuation objects.

Another advantage resides in automatically correcting image artifacts inimages including a metal or other high density region and a bone orother medium density region.

Another advantage resides in correct radiation therapy planning for thesubjects containing highly attenuating objects.

Another advantage resides in applicability of the present application toboth original sinogram data and otherwise available reconstructedimages.

Numerous additional advantages and benefits will become apparent tothose of ordinary skill in the art upon reading the following detaileddescription of the preferred embodiments.

The invention may take form in various components and arrangements ofcomponents, and in various process operations and arrangements ofprocess operations. The drawings are only for the purpose ofillustrating preferred embodiments and are not to be construed aslimiting the invention.

FIG. 1 diagrammatically shows a computed tomography imaging systemincluding an artifact-correcting process according to the presentapplication;

FIG. 2 diagrammatically shows an expanded portion of the computedtomography imaging system including an artifact-correcting process;

FIG. 3A diagrammatically shows an uncorrected reconstructed image;

FIG. 3B diagrammatically shows a classified image; and

FIG. 3C diagrammatically shows a metal-free image.

With reference to FIG. 1, a imaging system 10 includes a computedtomography scanner 12 having a radiation source 14 that produces aradiation beam directed into an examination region 16. The radiationbeam interacts with and is partially absorbed as it traverses a regionof interest of an imaging subject disposed in the examination region 16,producing spatially varying absorption of the radiation as it passesthrough the examination region. A radiation detector 18 detects theabsorption-attenuated radiation after it passes through the examinationregion 16.

Preferably, the radiation source 14 produces a fan-beam or cone-beam ofx-rays. The radiation source 14 and the detector 18 are preferablymounted in oppositely facing fashion on a rotating gantry 20 so that thedetector 18 continuously receives x-rays from the radiation source 14.As the source 14 and the detector 18 rotate about the examination region16 on the rotating gantry 20, views are acquired over an angular rangeof preferably about 360° or more. Optionally, a reduced scan of betweenabout 180° and 360° is used. In one embodiment, the detector 18 isreplaced by a stationary detector ring mounted on a stationary gantry22. Typically, a subject support 26 is linearly movable in an axial orz-direction by a motor means 28.

Multiple-slice computed tomography projection data are acquired byperforming successive axial scans with the subject support 26 beingstationary during each axial scan and stepped linearly between axialscans. In this arrangement, the detector 18 can have either a single rowof detector elements (that is, a one-dimensional detector) or atwo-dimensional array of detector elements. Alternatively, helicalcomputed tomography projection data are acquired during continuouslinear movement of the subject support 26 and simultaneous rotation ofthe gantry 20.

The outputs of detector elements of the radiation detector 18 areconverted to electric acquired integrated attenuation projection valuesμd_(o) that are stored in a data memory 30. Each projection datum μd_(o)corresponds to a line integral of attenuation along a line from theradiation source 14 to a corresponding one of the detector elements ofthe detector 18. The projection data can be represented in a sinogramformat in which each two-dimensional slice of the imaged region ofinterest is represented by a projection data array having coordinates ofviewing angle (p) and line integral index (n).

For typical fan-beam and cone-beam geometries, the line integral index ntypically corresponds to a detector index indicating a detector elementused to measure the projection of index n. It is contemplated, however,that the line integral index n may lack a direct correspondence withdetector element number. Such a lack of direct correspondence canresult, for example, from interpolation between rebinned projections.

With continuing reference to FIG. 1, a slice cycling processor 32 cyclesthrough the sinograms corresponding to spatial slices and successivelyinputs each sinogram into a reconstruction processor 34. Thereconstruction processor 34 reconstructs the input data into a 3Duncorrected tomographic image T which is stored in an uncorrected 3Dimage memory 36. Although picture elements of a 2D or slice image arecommonly denoted as “pixels”, and elements of a 3D or volume image areoften denoted as “voxels”; “pixels” is used herein generally to refer topicture elements of both 2D and 3D images.

With reference to FIG. 3A, the presence of one or more high densityregions 40 in the slice typically causes the uncorrected reconstructedimage T to include metal artifacts which generally manifest in the imageas streaks 42 extending away from the high density region 40. Thediagrammatic reconstructed image T of FIG. 3A is not intended tocorrespond to images of any particular anatomical structure, but ratherdiagrammatically illustrate features of the artifact-correctingreconstruction process that are typically observable in image space.

With reference again to FIG. 1, a correction means or processor oralgorithm 44 performs artifacts correction, in which artifactsintroduced by high density regions such as metal clips, high-densitydental fillings, or the like, are substantially corrected, even forimages that contain discontinuous high density regions or one or moremedium density regions in addition to the one or more high densityregions. As described in a greater detail below, the correctionprocessor 44 receives an uncorrected image data and outputs a correctedimage data which is consequently reconstructed by the reconstructionprocessor 34 into a corrected 3D tomographic image representation T′.Spatially successive artifact-corrected reconstructed image slices,slabs or volumes are accumulated in a corrected 3D image memory 46 todefine a three-dimensional artifact-corrected reconstructed volumeimage. If, however, the acquired projection data is limited to a singleslice of the region of interest, then the acquired projection datacorresponding to the single slice is processed by the reconstructionprocessor 34 and the corrected 3D image memory 46 stores atwo-dimensional artifact-corrected reconstructed image. Optionally,projection data corresponding to one or more image slices are acquiredover a selected time interval to provide a temporal series ofartifact-corrected reconstructed image slices or image volumesrepresentative of a temporal evolution of the region of interest.

A video processor 50 processes some or all of the contents of thecorrected 3D image memory 46 or, optionally, of the uncorrected 3D imagememory 36 to create a human-viewable image representation such as athree-dimensional rendering, a selected image slice, a maximum intensityprojection, a CINE animation, or the like. The human-viewable imagerepresentation is displayed on a display 52 of a user interface 54,which is preferably a personal computer, a workstation, a laptopcomputer, or the like. Optionally, selected contents of image memory 36,46 are printed on paper, stored in a non-volatile electronic or magneticstorage medium, transmitted over a local area network or the Internet,or otherwise processed. Preferably, a radiologist or other operatorcontrols the computed tomography imaging scanner 12 via an input means56 to program a scan controller 58 to set up an imaging session, modifyan imaging session, execute an imaging session, monitor an imagingsession, or otherwise operate the scanner 12.

With continuing reference to FIG. I and further reference to FIG. 2, thecorrection means 44 receives the filtered uncorrected tomographic imagedata T and applies a clustering technique or process 60 to each pixel inthe image data set. The data is clustered into classes so that the datawithin each class is more similar than those outside that class or inanother class. In one embodiment, in which the original sinogram data Sis not available, the uncorrected tomographic image T is initiallyfiltered by a tomogram filter means 62, which applies a low pass filterto harmonize noise. Preferably, the filter means 62 applies atwo-dimensional Gaussian filter with a 5 by 5 pixel mask and a standarddeviation (sigma) of 1. The filtered uncorrected tomographic image T isreceived as the input by the correction means 44 to compensate for metalartifacts.

With continuing reference to FIG. 2 and further reference to FIG. 3B,the clustering means 60 partitions the uncorrected tomographic imagedata T into the clusters using a statistical classifier. Preferably, theclustering means 60 employs a statistical k-means classifier. Moreparticularly, a k-means 64 supplies a number of classes and each classproperties to the clustering means 60. Preferably, at least four defaultclasses are identified in advance by the user: metal class “4”, bonesclass “3”, soft tissue class “2”, and air class “1”. Alternatively,fixed sets of settings are identified for particular applications andused automatically. The initial mean value for each class isautogenerated and preassigned. Optionally, each class is assigned anequal number of pixels. Optionally, the classes and initial values aredefined interactively by the user. The user interactively intervenes,when required, to redefine and reassign the classes and initial values.A segmentation or classifying means or algorithm 66 classifies pixels inthe uncorrected tomographic image T into the identified classes togenerate a segmented or classified image T_(class) in which pixel valuesare replaced by class classification index values corresponding to themetal, bone, soft tissue, and air classes.

With continuing reference to FIG. 3B, the pixel classified imageT_(class), which is diagrammatically shown in FIG. 3B, has regions ofthe image essentially consisting of pixels of a particular class labeledby an appropriate class index selected from the class indices “4”, “3”,“2”, “1”. The exemplary image includes a region of high density or metalclass “4” which could be a metal implant, a region of medium density orbone class “3” which could be a region of bone, both contained within aregion of low density or tissue class “2” which could be soft tissue orthe like. A region of air density or air class “1” fills the peripheryof the pixel density-classified image T_(class) corresponding, forexample, to the ambient air surrounding the imaging subject.

With reference again to FIG. 2, the clustering means 60 determines thecentroid of each cluster. The clustering means 60 utilizes an iterativealgorithm which minimizes the sum over all clusters of within-clustersums of pixel value-to-cluster centroid distance. As a result, some ofthe pixels are reassigned to different clusters. New centroids aredetermined as the input to minimize. The clustering process 60 iscontinued until the optimum assignment of all pixels is found. Theresult is a vector containing the cluster indices of each pixel and avector containing the cluster values. The classified pixel data arestored in a classified pixel memory 68. It is contemplated that otherstatistical classifiers such as c-mean, fuzzy c-mean, unsupervisedBayesian, and the like might be used.

A metal pixels replacement means 70 assigns a surrounding tissue classvalue to the pixels or voxels classified into the metal class.Preferably, the bone class value is predetermined as a default value forsuch pixels. Of course, it is also contemplated that the tissue classvalue or other class value might be assigned to such pixels. Thefinalized classified pixels are stored in a final classified pixelmemory 72.

With reference to FIG. 3C, in the final classified image T_(class) _(—)_(final) the regions of high density “4” are effectively removed byreplacing pixels in these regions with the grayscale value for pixels ofthe medium density class “3”, e.g. bone.

With reference again to FIG. 2, although not shown in FIG. 3C, one ormore of the regions may have discontinuances, sharp points, or otheranatonomically incorrect shapes. A morphological means or algorithm 80performs morphological operations on the image to generate a tomogrammodel image T_(model). Optionally, the user selects selected regions,e.g. the regions containing severe artifacts, to perform morphologicaloperations on. Preferably, the morphological means 80 uses openingand/or closing algorithms or operators 82, 84 to restore the imagecontent. The opening and closing algorithms 82, 84 use prior knowledgewhich is inserted into the image to eliminate unwanted artifacts whichare known not to be present in the soft tissue by the virtue of humananatomy. For example, it is known that there is no sharp edges in thesoft tissue; thus, prior knowledge that there is no sharp edges isincorporated. Preferably, the morphological means 80 eliminates airbubbles, narrow spikes, and other inappropriate data that is known notto be present in the soft issue. Preferably, the shape of theopening/closing operators 82, 84 is a disk with a size between five toeight pixels. Preferably, the user sets the default size and shape, e.g.a disk of six pixels wide, ahead of time. In one embodiment, the userinteractively modifies the default size and shape depending on theartifact level: the stronger the artifacts are, the bigger the size ofthe operator 82, 84 is set at. The resultant tomogram model imageT_(model) is stored in a tomogram model memory 86. A forward projectionmeans 88 forward projects the tomogram model image T_(model) to producea sinogram model image S_(model) which is stored in a sinogram modelimage memory 90.

A corrupted rays identifying means 100 identifies rays or class “4”regions in the original sinogram data S that pass through the metal,i.g. the corrupted rays to be corrected. Preferably, the corrupted raysidentifying means 100 identifies edges/boundaries of regions consistingof the pixels of the high density by one of known techniques after themetal classified pixels are forward projected onto a sinogram.

A corrupted rays replacement means 102 replaces the identified corruptedrays of the original sinogram data S, e.g. projection data correspondingto the rays passing through high density regions, with the projectiondata from the sinogram model S_(model) to produce a corrected sinogramimage S′. Preferably, the corrupted rays replacement means 102calculates a linear offset or other smooth transition that enables asmooth integration of the corresponding data portions of the sinogrammodel into the original sinogram. In this manner, the corrupted regionsof the original sinogram image S are replaced by the correspondingregions from the sinogram model S_(model).

The metal-free, corrected sinogram image S′ is stored in a correctedsinogram image memory 104.

With reference again to FIG. 1, the reconstruction processor 34 uses astandard filtered backprojection means or algorithm 110 to generate acorrected tomographic image. The backprojection means 110 backprojectsthe corrected sinogram S′ to obtain corresponding intermittent correctedtomographic image T′_(int). Preferably, the reconstruction processor 34uses a Ramachandran-Lakshminarayanan filter. To get a clearer view ofthe corrections, an extraction means 112 extracts the metal segmentsfrom the original uncorrected tomographic image T. A combiner 114superimposes or combines the extracted metal segments with theintermittent corrected tomographic image T′_(int) to generate a finalcorrected tomographic image T′ which is stored in the corrected 3D imagememory 46.

Preferably, the correction process 44 is iteratively repeated such thatthe classes are redefined and modified. For example, on a subsequentiteration the k-means 64 supplies five classes instead of four classesto further redefine and differentiate data.

The invention has been described with reference to the preferredembodiments. Obviously, modifications and alterations will occur toothers upon reading and understanding the preceding detaileddescription. It is intended that the invention be construed as includingall such modifications and alterations insofar as they come within thescope of the appended claims or the equivalents thereof.

1. A diagnostic imaging system which automatically corrects metalartifacts in an uncorrected tomographic image caused by high attenuatingobjects, the system comprising: a means for clustering pixels of thefiltered uncorrected tomographic image, which clustering means includes:a means for classifying pixels of the filtered uncorrected reconstructedimage into at least metal, bone, tissue, and air pixel classes togenerate a classified image; a means for replacing metal class pixels ofthe classified image with pixel values of another pixel class togenerate a metal free classified image; a means for forward projectingthe metal free classified image to generate a model projection data; ameans for identifying corrupted regions of original projection datacontributing to the pixels of the metal class; and a means for replacingthe identified corrupted regions with corresponding regions of the modelprojection data to generate corrected projection data which isreconstructed by a reconstruction means into a corrected reconstructedimage.
 2. The system as set forth in claim 1, further including: amorphological means for using prior knowledge to refine class regions ofthe metal free classified image.
 3. The system as set forth in claim 2,wherein the morphological means removes at least one of bubbles, points,and sharp edges from the metal free classified image.
 4. The system asset forth in claim 1, further including: a k-means for providing atleast one of a class definition, number of classes, and initialgrayscale value for each class.
 5. The system as set forth in claim 1,wherein the clustering means uses one of k-mean classifier, c-meanclassifier, fuzzy c-mean classifier, and unsupervised Bayesianclassifier cluster pixels into the classes.
 6. The system as set forthin claim 1, wherein the clustering means receives the reconstructedimage for iteratively improving the corrected reconstructed image. 7.The system as set forth in claim 6, wherein the clustering means refinescorrection of the metal artifacts by iteratively modifying at least oneof a class definition, number of classes and an initial grayscale valueof at least one class.
 8. The system as set forth in claim 1, furtherincluding: a user input means by which a user defines at least one of aclass definition, number of classes and an initial value of at least oneclass.
 9. The system as set forth in claim 1, wherein the corruptedregions replacing means interpolatively adjusts the model projectiondata to smooth transitions between the model projection data and theprojection data.
 10. A method for automatically correcting metalartifacts in an uncorrected tomographic image caused by high attenuatingobjects, comprising: clustering pixels of the filtered uncorrectedtomographic image; classifying pixels of the filtered uncorrectedreconstructed image into at least metal, bone, tissue, and air pixelclasses to generate a classified image; replacing metal class pixels ofthe classified image with pixel values of another pixel class togenerate a metal free classified image; forward projecting the metalfree classified image to generate a model projection data; identifyingcorrupted regions of original projection data contributing to the pixelsof the metal class; replacing the identified corrupted regions withcorresponding regions of the model projection data to generate correctedprojection data; and reconstructing the corrected projection data into acorrected reconstructed image.
 11. The method as set forth in claim 10,wherein the pixels are clustered iteratively by a use of an iterativeclassifier function.
 12. The method as set forth in claim 11, whereinthe classifier function is one of k-mean classifier, c-mean classifier,fuzzy c-mean classifier, and unsupervised Bayesian classifier.
 13. Themethod as set forth in claim 10, further including: using priorknowledge to refine class regions of the metal free classified image.14. The method as set forth in claim 10, further including: removing atleast one of bubbles, points, and sharp edges from the metal freeclassified image.
 15. The method as set forth in claim 10, wherein thereconstructing the corrected projection data into the correctedreconstructed image includes: reconstructing the corrected projectiondata using filtered backprojection.
 16. The method as set forth in claim10, wherein the original projection data is reconstructed by applyingRadon transform to the uncorrected tomographic image and the corruptedregions are identified and replaced in the reconstructed originalprojection data.
 17. A diagnostic imaging system including: areconstruction processor which reconstructs projection data into areconstructed image; a filter which reduces and harmonizes noise of theuncorrected tomographic image a classifying algorithm which classifiespixels of the uncorrected tomographic image at least into metal, bone,tissue, and air pixel classes; a pixel replacement algorithm whichreplaces pixels of the reconstructed image that are classified into themetal class with pixel values of at least one other class to generate ametal free image ; a morphological algorithm which applies priorknowledge to the metal free image to refine classification regions ofthe metal free image based on known characteristics of subject anatomy;a forward projection algorithm which forward projects the metal freeimage to generate model projection data; and a replacement algorithmwhich replaces corrupted portions of the projection data which corruptedportions contribute to the pixels of the metal class with correspondingportions of the model projection data to generate corrected projectiondata , which is reconstructed by the reconstruction processor into acorrected tomographic image.
 18. A tomographic imaging systemcomprising: a classifying algorithm which classifies pixels of anuncorrected tomographic image into at least metal, bone, tissue, and airpixel classes; a pixel replacement algorithm which replaces pixels thatare classified into the metal class with pixel values of at least oneother class to generate a metal free tomographic image; and amorphological algorithm which applies prior knowledge to the metal freeimage to refine classification regions of the metal free image based onknown characteristics of subject anatomy.
 19. The tomographic imagingsystem of claim 18, wherein the morphological algorithm removes at leastone of bubbles, points, and sharp edges from the metal free image. 20.The tomographic imaging system of claim 18 further comprising: a forwardprojection algorithm which forward projects the metal free image togenerate model projection data; and a replacement algorithm whichreplaces corrupted portions of the projection data which corruptedportions contribute to the pixels of the metal class with correspondingportions of the model projection data to generate corrected projectiondata, which is reconstructed by a reconstruction processor into acorrected tomographic image.